Generally, my research interests are in computational genomics and
machine learning.
Specifically, my work focuses on the development and
application of new statistical learning algorithms to complex problems in
regulatory genomics and systems biology. Currently, I am especially, but not
exclusively, interested in:

understanding how information flows in the brain during sensory
processing and learning tasks,

identifying imprinted genes, their regulatory mechanisms, and their
implications for disease, and

improving the diagnosis and treatment of disease using high-throughput
clinical data.

Although these problems are quite diverse, a number of common themes appear
repeatedly throughout my work: probabilistic representations, Bayesian
statistics, fusion of information from multiple sources, optimization of
joint objective functions, and learning in high-dimensional spaces without
over-fitting. Many of these themes are variations on two simple ideas:
careful attention to biology in the development of statistical models and
the use of informative Bayesian priors to both regularize and guide
automated learning.